REGIONAL COVID-19 STATISTICS
Saunders
Updated: 2020-07-27 13:38:27 PDT
Original version created 2020-05-03. See below for revision history
Intro
The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.
This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.
This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.
National Maps
There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.
Cases and Deaths per Capita
This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.
Contained and Uncontained Disease
A key indicator of mitigation is capping infection. Uncontained disease growth threatens epidemic conditions.
This visualization shows places where current disease levels are below their peak levels (’contained“) and where current disease levels are at an all time high (”uncontained").
Reproduction and Control
\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).
After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.
\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]
| state | R_e | cases | daily_cases |
|---|---|---|---|
| Mississippi | 1.27 | 52294 | 1449 |
| North Dakota | 1.27 | 5794 | 136 |
| Montana | 1.26 | 3364 | 144 |
| Missouri | 1.25 | 38272 | 1108 |
| Wyoming | 1.25 | 2489 | 54 |
| Kentucky | 1.23 | 27951 | 708 |
| Oklahoma | 1.23 | 31113 | 937 |
| Tennessee | 1.18 | 91859 | 2530 |
| Arkansas | 1.17 | 37542 | 854 |
| Indiana | 1.17 | 63855 | 913 |
| Virginia | 1.17 | 68174 | 906 |
| West Virginia | 1.17 | 6022 | 155 |
| Alabama | 1.16 | 80765 | 2221 |
| California | 1.16 | 464941 | 11253 |
| Colorado | 1.16 | 44217 | 603 |
| Nevada | 1.16 | 43723 | 1306 |
| Nebraska | 1.15 | 24461 | 267 |
| New Mexico | 1.15 | 19183 | 341 |
| Wisconsin | 1.15 | 49209 | 1037 |
| Connecticut | 1.14 | 48526 | 111 |
| Georgia | 1.14 | 153002 | 3729 |
| Idaho | 1.14 | 18883 | 644 |
| Illinois | 1.14 | 171652 | 1415 |
| Maryland | 1.14 | 84082 | 840 |
| Florida | 1.13 | 432390 | 13287 |
| Louisiana | 1.13 | 108293 | 2468 |
| Minnesota | 1.13 | 50832 | 717 |
| New Hampshire | 1.12 | 6433 | 30 |
| Ohio | 1.10 | 84876 | 1529 |
| Oregon | 1.10 | 16965 | 373 |
| Texas | 1.10 | 408366 | 11135 |
| Washington | 1.10 | 55146 | 984 |
| New Jersey | 1.09 | 180447 | 306 |
| North Carolina | 1.09 | 113964 | 2185 |
| South Dakota | 1.09 | 8180 | 65 |
| Kansas | 1.08 | 26090 | 514 |
| Maine | 1.08 | 3808 | 20 |
| Pennsylvania | 1.08 | 112073 | 945 |
| South Carolina | 1.08 | 82903 | 2034 |
| Delaware | 1.07 | 14118 | 108 |
| Michigan | 1.07 | 86692 | 743 |
| Utah | 1.07 | 38385 | 677 |
| Iowa | 1.06 | 42670 | 573 |
| Massachusetts | 1.06 | 115222 | 296 |
| Vermont | 1.04 | 1404 | 8 |
| Arizona | 1.02 | 163681 | 3018 |
| New York | 1.02 | 416617 | 732 |
| Rhode Island | 1.00 | 16723 | 73 |
National Statistics
Total & Active Cases, and Deaths
These trend charts show the national disease statistics. They’re plotted against linear scales. While this hides some important details, the plots are more intuitively interpretable for most people.
## Warning: Removed 1 row(s) containing missing values (geom_path).
Mortality Trend
National Reproduction Rates \(R_e\)
There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.
Trend
Distribution of \(R_e\) Values
Regional Snapshots
Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.
How to read the charts
There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.
(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).
Washington and Oregon
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Okanogan | WA | 14 | 1 | 1.5 | 647 | 1550 | 48 |
| King | WA | 1 | 2 | 1.1 | 14446 | 670 | 196 |
| Spokane | WA | 6 | 3 | 1.2 | 3366 | 680 | 105 |
| Pierce | WA | 4 | 4 | 1.2 | 4868 | 570 | 107 |
| Kitsap | WA | 16 | 5 | 1.3 | 509 | 190 | 21 |
| Douglas | WA | 13 | 6 | 1.3 | 651 | 1570 | 26 |
| Chelan | WA | 10 | 7 | 1.2 | 936 | 1240 | 33 |
| Snohomish | WA | 3 | 8 | 1.1 | 5349 | 680 | 62 |
| Benton | WA | 5 | 9 | 1.0 | 3392 | 1750 | 65 |
| Yakima | WA | 2 | 10 | 0.9 | 10154 | 4070 | 104 |
| Franklin | WA | 7 | 11 | 1.0 | 3206 | 3540 | 51 |
| Grant | WA | 9 | 12 | 1.0 | 1148 | 1210 | 23 |
| Clark | WA | 8 | 14 | 1.0 | 1702 | 370 | 34 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Multnomah | OR | 1 | 1 | 1.1 | 3999 | 500 | 84 |
| Umatilla | OR | 4 | 2 | 1.1 | 1726 | 2240 | 54 |
| Marion | OR | 3 | 3 | 1.1 | 2423 | 720 | 44 |
| Washington | OR | 2 | 4 | 1.1 | 2522 | 430 | 50 |
| Deschutes | OR | 8 | 5 | 1.2 | 459 | 250 | 18 |
| Jefferson | OR | 13 | 6 | 1.3 | 257 | 1110 | 9 |
| Clackamas | OR | 5 | 7 | 1.1 | 1278 | 310 | 22 |
| Lane | OR | 7 | 11 | 1.0 | 461 | 120 | 12 |
| Malheur | OR | 6 | 14 | 0.8 | 600 | 1970 | 15 |
| Union | OR | 9 | 23 | 0.6 | 388 | 1490 | 1 |
## Warning: Removed 1 rows containing missing values (geom_col).
California
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Kern | CA | 6 | 1 | 1.8 | 16192 | 1830 | 1239 |
| Los Angeles | CA | 1 | 2 | 1.1 | 176498 | 1750 | 3188 |
| San Bernardino | CA | 4 | 3 | 1.2 | 29600 | 1390 | 891 |
| Riverside | CA | 2 | 4 | 1.1 | 35180 | 1480 | 738 |
| Fresno | CA | 7 | 5 | 1.2 | 12934 | 1320 | 406 |
| Orange | CA | 3 | 6 | 1.0 | 35087 | 1110 | 794 |
| San Joaquin | CA | 8 | 7 | 1.2 | 10595 | 1450 | 338 |
| San Diego | CA | 5 | 8 | 1.1 | 27410 | 830 | 576 |
| Alameda | CA | 9 | 16 | 1.1 | 10527 | 640 | 203 |
Four Corners
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Maricopa | AZ | 1 | 1 | 1.0 | 109942 | 2580 | 2180 |
| Pima | AZ | 2 | 2 | 1.0 | 15061 | 1480 | 243 |
| Yuma | AZ | 3 | 3 | 1.0 | 10297 | 4950 | 145 |
| Pinal | AZ | 4 | 4 | 1.0 | 7543 | 1800 | 138 |
| Mohave | AZ | 8 | 5 | 1.1 | 2790 | 1350 | 72 |
| Gila | AZ | 12 | 6 | 1.3 | 702 | 1310 | 24 |
| Apache | AZ | 6 | 7 | 1.1 | 2919 | 4080 | 27 |
| Navajo | AZ | 5 | 9 | 0.9 | 5139 | 4730 | 54 |
| Santa Cruz | AZ | 9 | 10 | 1.1 | 2513 | 5390 | 27 |
| Coconino | AZ | 7 | 12 | 0.9 | 2882 | 2060 | 30 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Denver | CO | 1 | 1 | 1.2 | 9118 | 1310 | 106 |
| El Paso | CO | 4 | 2 | 1.1 | 4183 | 610 | 90 |
| Arapahoe | CO | 2 | 3 | 1.1 | 6498 | 1020 | 70 |
| Adams | CO | 3 | 4 | 1.1 | 5547 | 1120 | 66 |
| Jefferson | CO | 5 | 5 | 1.2 | 3623 | 640 | 51 |
| Chaffee | CO | 17 | 6 | 1.4 | 278 | 1450 | 17 |
| Larimer | CO | 9 | 7 | 1.1 | 1265 | 370 | 28 |
| Douglas | CO | 8 | 8 | 1.1 | 1524 | 460 | 30 |
| Weld | CO | 6 | 10 | 1.0 | 3420 | 1160 | 30 |
| Boulder | CO | 7 | 12 | 1.1 | 1785 | 560 | 19 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Salt Lake | UT | 1 | 1 | 1.0 | 18332 | 1640 | 271 |
| Utah | UT | 2 | 2 | 1.1 | 7236 | 1230 | 152 |
| Davis | UT | 3 | 3 | 1.1 | 2757 | 810 | 71 |
| Washington | UT | 5 | 4 | 1.1 | 2181 | 1360 | 46 |
| Weber | UT | 4 | 5 | 1.0 | 2380 | 960 | 60 |
| Cache | UT | 6 | 6 | 1.1 | 1747 | 1430 | 11 |
| Tooele | UT | 10 | 7 | 1.0 | 499 | 770 | 11 |
| San Juan | UT | 8 | 8 | 1.0 | 588 | 3850 | 10 |
| Summit | UT | 7 | 11 | 0.9 | 675 | 1670 | 7 |
| Wasatch | UT | 9 | 12 | 0.9 | 517 | 1690 | 4 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Bernalillo | NM | 1 | 1 | 1.1 | 4466 | 660 | 112 |
| Lea | NM | 7 | 2 | 1.4 | 528 | 750 | 25 |
| Rio Arriba | NM | 12 | 3 | 1.3 | 294 | 750 | 15 |
| Doña Ana | NM | 4 | 4 | 1.1 | 2024 | 940 | 44 |
| Otero | NM | 5 | 5 | 1.3 | 1037 | 1580 | 9 |
| Valencia | NM | 10 | 6 | 1.2 | 347 | 460 | 13 |
| McKinley | NM | 2 | 7 | 1.1 | 3905 | 5360 | 21 |
| Santa Fe | NM | 8 | 9 | 1.1 | 505 | 340 | 13 |
| Sandoval | NM | 6 | 10 | 1.1 | 1011 | 720 | 14 |
| Curry | NM | 9 | 12 | 1.1 | 372 | 740 | 10 |
| San Juan | NM | 3 | 16 | 0.9 | 2934 | 2300 | 14 |
Mid-Atlantic
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Middlesex | NJ | 4 | 1 | 1.2 | 17717 | 2140 | 41 |
| Ocean | NJ | 7 | 2 | 1.2 | 10238 | 1730 | 29 |
| Camden | NJ | 9 | 3 | 1.1 | 8085 | 1590 | 28 |
| Gloucester | NJ | 16 | 4 | 1.1 | 2956 | 1020 | 16 |
| Bergen | NJ | 1 | 5 | 1.0 | 20473 | 2200 | 26 |
| Burlington | NJ | 12 | 6 | 1.1 | 5609 | 1260 | 16 |
| Monmouth | NJ | 8 | 7 | 1.0 | 9943 | 1590 | 26 |
| Essex | NJ | 3 | 8 | 1.0 | 19521 | 2460 | 19 |
| Passaic | NJ | 5 | 10 | 1.0 | 17438 | 3460 | 16 |
| Union | NJ | 6 | 15 | 0.9 | 16749 | 3030 | 8 |
| Hudson | NJ | 2 | 16 | 0.8 | 19532 | 2920 | 14 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Allegheny | PA | 4 | 1 | 1.0 | 7484 | 610 | 188 |
| Philadelphia | PA | 1 | 2 | 1.1 | 29431 | 1870 | 146 |
| Delaware | PA | 3 | 3 | 1.1 | 8267 | 1470 | 56 |
| Bucks | PA | 5 | 4 | 1.1 | 6688 | 1070 | 48 |
| Chester | PA | 9 | 5 | 1.1 | 4632 | 900 | 47 |
| Montgomery | PA | 2 | 6 | 1.1 | 9470 | 1150 | 48 |
| Armstrong | PA | 40 | 7 | 1.4 | 156 | 240 | 8 |
| Berks | PA | 7 | 9 | 1.1 | 5007 | 1200 | 26 |
| Lancaster | PA | 6 | 11 | 1.0 | 5301 | 980 | 36 |
| Lehigh | PA | 8 | 17 | 1.0 | 4677 | 1290 | 19 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Baltimore | MD | 3 | 1 | 1.2 | 10865 | 1310 | 176 |
| Baltimore city | MD | 4 | 2 | 1.1 | 10335 | 1680 | 145 |
| Prince George’s | MD | 1 | 3 | 1.1 | 21965 | 2420 | 137 |
| Montgomery | MD | 2 | 4 | 1.0 | 17025 | 1640 | 95 |
| Anne Arundel | MD | 5 | 5 | 1.1 | 6380 | 1120 | 67 |
| Harford | MD | 9 | 6 | 1.2 | 1591 | 630 | 27 |
| Worcester | MD | 16 | 7 | 1.3 | 499 | 970 | 16 |
| Howard | MD | 6 | 9 | 1.1 | 3356 | 1060 | 40 |
| Frederick | MD | 7 | 10 | 1.2 | 2886 | 1160 | 23 |
| Charles | MD | 8 | 12 | 1.1 | 1729 | 1100 | 17 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Virginia Beach city | VA | 5 | 1 | 1.3 | 3551 | 790 | 162 |
| Patrick | VA | 64 | 2 | 1.9 | 91 | 510 | 6 |
| Norfolk city | VA | 7 | 3 | 1.2 | 2837 | 1160 | 115 |
| Newport News city | VA | 9 | 4 | 1.2 | 1491 | 830 | 65 |
| Suffolk city | VA | 14 | 5 | 1.2 | 922 | 1030 | 32 |
| Hampton city | VA | 15 | 6 | 1.2 | 896 | 660 | 34 |
| Fairfax | VA | 1 | 7 | 1.0 | 15318 | 1340 | 64 |
| Chesterfield | VA | 4 | 8 | 1.1 | 3734 | 1100 | 41 |
| Henrico | VA | 6 | 9 | 1.1 | 3266 | 1000 | 34 |
| Prince William | VA | 2 | 10 | 1.0 | 8464 | 1850 | 47 |
| Loudoun | VA | 3 | 13 | 1.0 | 4840 | 1260 | 29 |
| Arlington | VA | 8 | 26 | 0.9 | 2818 | 1220 | 13 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Kanawha | WV | 2 | 1 | 1.3 | 686 | 370 | 26 |
| Logan | WV | 23 | 2 | 1.6 | 77 | 230 | 5 |
| Mingo | WV | 18 | 3 | 1.5 | 98 | 400 | 8 |
| Ohio | WV | 6 | 4 | 1.2 | 234 | 550 | 8 |
| Cabell | WV | 5 | 5 | 1.2 | 276 | 290 | 7 |
| Hancock | WV | 20 | 6 | 1.2 | 90 | 300 | 5 |
| Raleigh | WV | 14 | 7 | 1.2 | 135 | 180 | 5 |
| Monongalia | WV | 1 | 9 | 0.8 | 892 | 850 | 23 |
| Berkeley | WV | 3 | 11 | 1.0 | 613 | 540 | 6 |
| Wayne | WV | 9 | 12 | 1.0 | 181 | 440 | 4 |
| Jefferson | WV | 4 | 19 | 0.8 | 284 | 510 | 2 |
| Wood | WV | 7 | 20 | 0.6 | 229 | 270 | 2 |
| Randolph | WV | 8 | 22 | 0.6 | 209 | 720 | 1 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| New Castle | DE | 1 | 1 | 1.1 | 6472 | 1170 | 54 |
| Sussex | DE | 2 | 2 | 1.0 | 5522 | 2520 | 32 |
| Kent | DE | 3 | 3 | 1.0 | 2124 | 1210 | 21 |
Deep South
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Baldwin | AL | 7 | 1 | 1.4 | 2784 | 1340 | 146 |
| Jefferson | AL | 1 | 2 | 1.1 | 10630 | 1610 | 305 |
| Mobile | AL | 2 | 3 | 1.2 | 7313 | 1760 | 200 |
| Calhoun | AL | 17 | 4 | 1.3 | 1118 | 970 | 56 |
| Madison | AL | 4 | 5 | 1.1 | 4532 | 1270 | 174 |
| Houston | AL | 16 | 6 | 1.3 | 1134 | 1090 | 47 |
| Shelby | AL | 6 | 7 | 1.1 | 2834 | 1340 | 91 |
| Montgomery | AL | 3 | 8 | 1.1 | 5759 | 2540 | 94 |
| Tuscaloosa | AL | 5 | 15 | 1.1 | 3601 | 1750 | 69 |
| Marshall | AL | 8 | 17 | 1.1 | 2732 | 2870 | 56 |
| Lee | AL | 9 | 20 | 1.1 | 2420 | 1520 | 56 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Jackson | MS | 6 | 1 | 1.5 | 1596 | 1120 | 88 |
| Alcorn | MS | 56 | 2 | 1.7 | 266 | 720 | 20 |
| Coahoma | MS | 35 | 3 | 1.5 | 556 | 2340 | 36 |
| Hinds | MS | 1 | 4 | 1.3 | 4495 | 1860 | 138 |
| Rankin | MS | 4 | 5 | 1.3 | 1827 | 1210 | 64 |
| Washington | MS | 9 | 6 | 1.3 | 1306 | 2770 | 51 |
| Harrison | MS | 5 | 7 | 1.2 | 1800 | 890 | 57 |
| DeSoto | MS | 2 | 11 | 1.2 | 2849 | 1620 | 74 |
| Madison | MS | 3 | 12 | 1.2 | 2047 | 1980 | 50 |
| Jones | MS | 7 | 19 | 1.2 | 1545 | 2260 | 28 |
| Forrest | MS | 8 | 20 | 1.2 | 1398 | 1850 | 32 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Allen | LA | 26 | 1 | 1.5 | 984 | 3830 | 56 |
| Calcasieu | LA | 6 | 2 | 1.1 | 5520 | 2760 | 192 |
| East Baton Rouge | LA | 3 | 3 | 1.1 | 9738 | 2190 | 210 |
| Vernon | LA | 37 | 4 | 1.4 | 592 | 1160 | 33 |
| Jefferson | LA | 1 | 5 | 1.0 | 13465 | 3090 | 178 |
| Acadia | LA | 15 | 6 | 1.2 | 2250 | 3600 | 79 |
| St. Landry | LA | 17 | 7 | 1.2 | 1903 | 2280 | 71 |
| Lafayette | LA | 4 | 8 | 1.0 | 5882 | 2450 | 154 |
| Caddo | LA | 5 | 11 | 1.1 | 5649 | 2270 | 104 |
| Tangipahoa | LA | 9 | 12 | 1.1 | 2819 | 2160 | 76 |
| St. Tammany | LA | 7 | 13 | 1.1 | 4299 | 1710 | 90 |
| Ouachita | LA | 8 | 18 | 1.1 | 4068 | 2610 | 76 |
| Orleans | LA | 2 | 22 | 1.0 | 9916 | 2540 | 94 |
FL and GA
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Miami-Dade | FL | 1 | 1 | 1.2 | 106328 | 3920 | 3540 |
| Columbia | FL | 30 | 2 | 1.7 | 2325 | 3360 | 206 |
| Broward | FL | 2 | 3 | 1.1 | 50618 | 2650 | 1743 |
| Wakulla | FL | 51 | 4 | 1.7 | 526 | 1650 | 47 |
| Palm Beach | FL | 3 | 5 | 1.1 | 30714 | 2120 | 787 |
| Bay | FL | 26 | 6 | 1.4 | 2967 | 1630 | 172 |
| Orange | FL | 4 | 7 | 1.1 | 27611 | 2090 | 714 |
| Duval | FL | 6 | 11 | 1.0 | 20232 | 2190 | 557 |
| Hillsborough | FL | 5 | 12 | 1.0 | 27599 | 2000 | 612 |
| Polk | FL | 9 | 14 | 1.1 | 11706 | 1750 | 349 |
| Lee | FL | 8 | 17 | 1.0 | 14983 | 2080 | 376 |
| Pinellas | FL | 7 | 21 | 1.0 | 15490 | 1620 | 318 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Fulton | GA | 1 | 1 | 1.2 | 16028 | 1570 | 445 |
| Cobb | GA | 4 | 2 | 1.2 | 10148 | 1360 | 270 |
| Wayne | GA | 46 | 3 | 1.5 | 574 | 1930 | 45 |
| Gwinnett | GA | 2 | 4 | 1.1 | 15456 | 1710 | 317 |
| Chatham | GA | 6 | 5 | 1.2 | 4352 | 1520 | 152 |
| DeKalb | GA | 3 | 6 | 1.1 | 11225 | 1510 | 229 |
| Richmond | GA | 9 | 7 | 1.2 | 2837 | 1410 | 99 |
| Clayton | GA | 7 | 8 | 1.1 | 4035 | 1450 | 98 |
| Hall | GA | 5 | 14 | 1.1 | 4937 | 2520 | 86 |
| Muscogee | GA | 8 | 24 | 1.0 | 3915 | 1990 | 82 |
Texas & Oklahoma
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Real | TX | 160 | 1 | 4.9 | 88 | 2600 | 25 |
| Jasper | TX | 120 | 2 | 2.3 | 178 | 500 | 17 |
| Runnels | TX | 165 | 3 | 2.2 | 80 | 780 | 9 |
| Bexar | TX | 3 | 4 | 1.2 | 38581 | 2000 | 1541 |
| Harris | TX | 1 | 5 | 1.1 | 66236 | 1440 | 1611 |
| Hidalgo | TX | 6 | 6 | 1.2 | 16271 | 1920 | 709 |
| Cameron | TX | 10 | 7 | 1.3 | 8261 | 1960 | 394 |
| Tarrant | TX | 4 | 10 | 1.1 | 25791 | 1280 | 610 |
| Dallas | TX | 2 | 11 | 1.0 | 47737 | 1850 | 1028 |
| Nueces | TX | 8 | 17 | 1.0 | 10914 | 3030 | 336 |
| El Paso | TX | 7 | 19 | 1.0 | 13498 | 1610 | 309 |
| Travis | TX | 5 | 24 | 0.9 | 20008 | 1660 | 326 |
| Galveston | TX | 9 | 31 | 0.9 | 8321 | 2540 | 163 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Jackson | OK | 12 | 1 | 1.8 | 386 | 1520 | 40 |
| Oklahoma | OK | 1 | 2 | 1.1 | 7735 | 990 | 234 |
| Tulsa | OK | 2 | 3 | 1.1 | 7524 | 1170 | 195 |
| Cleveland | OK | 3 | 4 | 1.2 | 2102 | 760 | 65 |
| Garfield | OK | 24 | 5 | 1.5 | 253 | 410 | 15 |
| Sequoyah | OK | 35 | 6 | 1.5 | 141 | 340 | 9 |
| Rogers | OK | 9 | 7 | 1.3 | 591 | 650 | 25 |
| Canadian | OK | 5 | 16 | 1.1 | 838 | 610 | 28 |
| Payne | OK | 8 | 22 | 1.2 | 597 | 730 | 10 |
| McCurtain | OK | 6 | 23 | 1.1 | 777 | 2360 | 13 |
| Comanche | OK | 7 | 26 | 1.1 | 670 | 550 | 13 |
| Texas | OK | 4 | 44 | 0.8 | 1016 | 4810 | 1 |
Michigan & Wisconsin
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Wayne | MI | 1 | 1 | 1.0 | 26116 | 1480 | 146 |
| Jackson | MI | 7 | 2 | 1.5 | 2294 | 1440 | 15 |
| Macomb | MI | 3 | 3 | 1.1 | 9120 | 1050 | 80 |
| Oakland | MI | 2 | 4 | 1.0 | 13989 | 1120 | 94 |
| Kent | MI | 4 | 5 | 1.0 | 6785 | 1050 | 68 |
| Genesee | MI | 5 | 6 | 1.1 | 3300 | 810 | 31 |
| Saginaw | MI | 8 | 7 | 1.1 | 1731 | 900 | 26 |
| Ottawa | MI | 9 | 11 | 1.0 | 1633 | 570 | 24 |
| Washtenaw | MI | 6 | 12 | 1.0 | 2760 | 750 | 24 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Waukesha | WI | 4 | 1 | 1.3 | 2970 | 740 | 121 |
| Milwaukee | WI | 1 | 2 | 1.1 | 18238 | 1910 | 345 |
| Racine | WI | 5 | 3 | 1.2 | 2913 | 1490 | 48 |
| Kenosha | WI | 6 | 4 | 1.1 | 2272 | 1350 | 43 |
| Marathon | WI | 17 | 5 | 1.2 | 487 | 360 | 19 |
| Marinette | WI | 27 | 6 | 1.3 | 243 | 600 | 14 |
| Waupaca | WI | 22 | 7 | 1.3 | 294 | 570 | 12 |
| Outagamie | WI | 9 | 9 | 1.2 | 959 | 520 | 26 |
| Brown | WI | 3 | 10 | 1.1 | 3779 | 1450 | 42 |
| Walworth | WI | 8 | 12 | 1.1 | 1059 | 1030 | 23 |
| Dane | WI | 2 | 13 | 0.9 | 3900 | 740 | 59 |
| Rock | WI | 7 | 16 | 1.1 | 1432 | 890 | 22 |
Minnesota, North Dakota, and South Dakota
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Hennepin | MN | 1 | 1 | 1.1 | 16228 | 1310 | 227 |
| Ramsey | MN | 2 | 2 | 1.1 | 6218 | 1150 | 81 |
| Anoka | MN | 4 | 3 | 1.2 | 2984 | 860 | 48 |
| Dakota | MN | 3 | 4 | 1.1 | 3451 | 830 | 62 |
| Beltrami | MN | 31 | 5 | 1.4 | 156 | 340 | 11 |
| Sherburne | MN | 17 | 6 | 1.3 | 526 | 560 | 16 |
| Washington | MN | 7 | 7 | 1.1 | 1668 | 660 | 32 |
| Scott | MN | 9 | 8 | 1.1 | 1171 | 820 | 24 |
| Olmsted | MN | 8 | 13 | 1.0 | 1525 | 1000 | 18 |
| Stearns | MN | 5 | 18 | 0.9 | 2732 | 1740 | 14 |
| Nobles | MN | 6 | 33 | 0.9 | 1727 | 7910 | 3 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Minnehaha | SD | 1 | 1 | 1.1 | 3989 | 2140 | 19 |
| Lincoln | SD | 4 | 2 | 1.3 | 465 | 850 | 7 |
| Pennington | SD | 2 | 3 | 0.9 | 793 | 730 | 11 |
| Davison | SD | 15 | 4 | 0.9 | 82 | 410 | 2 |
| Beadle | SD | 3 | 5 | 0.9 | 583 | 3170 | 2 |
| Union | SD | 6 | 6 | 0.8 | 176 | 1160 | 2 |
| Brown | SD | 5 | 7 | 0.8 | 377 | 970 | 2 |
| Codington | SD | 7 | 10 | 0.7 | 113 | 400 | 1 |
| Brookings | SD | 8 | 12 | 0.6 | 112 | 330 | 1 |
| Buffalo | SD | 9 | 13 | 0.5 | 108 | 5260 | 1 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Burleigh | ND | 2 | 1 | 1.3 | 755 | 810 | 30 |
| Williams | ND | 5 | 2 | 1.3 | 221 | 650 | 14 |
| Ward | ND | 6 | 3 | 1.4 | 147 | 210 | 8 |
| Morton | ND | 4 | 4 | 1.3 | 223 | 730 | 9 |
| Cass | ND | 1 | 5 | 1.0 | 2804 | 1610 | 25 |
| Grand Forks | ND | 3 | 6 | 1.2 | 596 | 850 | 13 |
| Stutsman | ND | 10 | 7 | 1.2 | 94 | 450 | 3 |
| Stark | ND | 7 | 8 | 1.1 | 145 | 470 | 4 |
| Walsh | ND | 9 | 9 | 0.9 | 100 | 930 | 4 |
| Mountrail | ND | 8 | 10 | 0.8 | 108 | 1060 | 3 |
Connecticut, Massachusetts, and Rhode Island
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Fairfield | CT | 1 | 1 | 1.2 | 17382 | 1840 | 40 |
| Hartford | CT | 3 | 2 | 1.2 | 12317 | 1380 | 35 |
| New Haven | CT | 2 | 3 | 1.0 | 12911 | 1500 | 23 |
| Litchfield | CT | 4 | 4 | 1.2 | 1554 | 850 | 4 |
| Tolland | CT | 7 | 5 | 1.1 | 962 | 640 | 3 |
| New London | CT | 6 | 6 | 0.7 | 1369 | 510 | 2 |
| Windham | CT | 8 | 7 | 0.7 | 662 | 570 | 2 |
| Middlesex | CT | 5 | 8 | 0.7 | 1369 | 840 | 2 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Middlesex | MA | 1 | 1 | 1.1 | 25312 | 1590 | 62 |
| Worcester | MA | 4 | 2 | 1.1 | 13072 | 1590 | 34 |
| Norfolk | MA | 5 | 3 | 1.1 | 9965 | 1430 | 39 |
| Suffolk | MA | 2 | 4 | 1.0 | 20876 | 2640 | 43 |
| Essex | MA | 3 | 5 | 1.0 | 16956 | 2170 | 35 |
| Bristol | MA | 7 | 6 | 1.0 | 8880 | 1590 | 29 |
| Hampden | MA | 8 | 7 | 1.0 | 7275 | 1550 | 21 |
| Barnstable | MA | 9 | 8 | 1.2 | 1689 | 790 | 10 |
| Plymouth | MA | 6 | 9 | 1.1 | 8977 | 1750 | 13 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Providence | RI | 1 | 1 | 1.0 | 14112 | 2220 | 60 |
| Kent | RI | 2 | 2 | 0.9 | 1365 | 830 | 7 |
| Washington | RI | 3 | 3 | 0.8 | 579 | 460 | 2 |
| Bristol | RI | 5 | 4 | 0.7 | 295 | 600 | 2 |
| Newport | RI | 4 | 5 | 0.5 | 372 | 450 | 1 |
New York
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| New York City | NY | 1 | 1 | 1.0 | 228511 | 2710 | 349 |
| Suffolk | NY | 3 | 2 | 1.0 | 42885 | 2880 | 59 |
| Nassau | NY | 2 | 3 | 1.0 | 42957 | 3170 | 46 |
| Erie | NY | 7 | 4 | 1.0 | 8313 | 900 | 41 |
| Westchester | NY | 4 | 5 | 1.0 | 35771 | 3690 | 35 |
| Albany | NY | 11 | 6 | 1.1 | 2448 | 800 | 18 |
| Monroe | NY | 8 | 7 | 1.0 | 4607 | 620 | 30 |
| Orange | NY | 6 | 11 | 1.0 | 11019 | 2910 | 11 |
| Dutchess | NY | 9 | 14 | 0.9 | 4429 | 1510 | 9 |
| Rockland | NY | 5 | 16 | 0.9 | 13849 | 4280 | 9 |
Vermont, New Hampshire, and Maine
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Chittenden | VT | 1 | 1 | 0.9 | 716 | 440 | 4 |
| Bennington | VT | 5 | 2 | 0.6 | 83 | 230 | 1 |
| Rutland | VT | 4 | 3 | 0.6 | 84 | 140 | 0 |
| Franklin | VT | 2 | 4 | 0.6 | 114 | 230 | 0 |
| Windham | VT | 3 | 5 | 0.4 | 102 | 240 | 0 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Cumberland | ME | 1 | 1 | 0.9 | 2015 | 690 | 10 |
| Androscoggin | ME | 3 | 2 | 1.1 | 529 | 490 | 2 |
| York | ME | 2 | 3 | 0.9 | 618 | 300 | 3 |
| Kennebec | ME | 4 | 4 | 0.9 | 157 | 130 | 1 |
| Penobscot | ME | 5 | 5 | 0.7 | 141 | 90 | 1 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Hillsborough | NH | 1 | 1 | 1.1 | 3658 | 890 | 20 |
| Rockingham | NH | 2 | 2 | 0.9 | 1583 | 520 | 4 |
| Merrimack | NH | 3 | 3 | 0.8 | 452 | 300 | 2 |
| Strafford | NH | 4 | 4 | 0.9 | 316 | 250 | 1 |
| Carroll | NH | 8 | 5 | 0.8 | 80 | 170 | 1 |
| Belknap | NH | 5 | 6 | 0.6 | 102 | 170 | 1 |
| Grafton | NH | 6 | 7 | 0.5 | 102 | 110 | 0 |
| Cheshire | NH | 7 | 8 | 0.3 | 82 | 110 | 0 |
Carolinas
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Richland | SC | 4 | 1 | 1.1 | 7192 | 1760 | 178 |
| Florence | SC | 11 | 2 | 1.2 | 2488 | 1800 | 77 |
| Hampton | SC | 43 | 3 | 1.5 | 263 | 1330 | 17 |
| Lexington | SC | 5 | 4 | 1.1 | 4269 | 1490 | 107 |
| Cherokee | SC | 31 | 5 | 1.4 | 472 | 830 | 23 |
| Greenville | SC | 2 | 6 | 1.0 | 9541 | 1910 | 177 |
| Charleston | SC | 1 | 7 | 1.0 | 10919 | 2770 | 225 |
| York | SC | 9 | 9 | 1.1 | 2885 | 1120 | 82 |
| Beaufort | SC | 8 | 10 | 1.1 | 3007 | 1650 | 84 |
| Berkeley | SC | 6 | 12 | 1.0 | 3577 | 1710 | 101 |
| Horry | SC | 3 | 16 | 0.9 | 7595 | 2370 | 123 |
| Spartanburg | SC | 7 | 18 | 1.0 | 3540 | 1170 | 78 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Mecklenburg | NC | 1 | 1 | 1.0 | 19462 | 1850 | 332 |
| Wake | NC | 2 | 2 | 1.0 | 10159 | 970 | 204 |
| Pitt | NC | 21 | 3 | 1.3 | 1461 | 820 | 47 |
| Chowan | NC | 83 | 4 | 1.6 | 95 | 670 | 7 |
| Cumberland | NC | 9 | 5 | 1.2 | 2325 | 700 | 62 |
| Buncombe | NC | 22 | 6 | 1.2 | 1454 | 570 | 52 |
| Guilford | NC | 4 | 7 | 1.1 | 4749 | 910 | 92 |
| Gaston | NC | 6 | 10 | 1.1 | 2735 | 1260 | 74 |
| Durham | NC | 3 | 12 | 1.0 | 5562 | 1810 | 73 |
| Forsyth | NC | 5 | 13 | 1.0 | 4605 | 1240 | 68 |
| Union | NC | 8 | 16 | 1.0 | 2532 | 1120 | 54 |
| Johnston | NC | 7 | 28 | 1.0 | 2658 | 1390 | 43 |
North-Rockies
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Big Horn | MT | 3 | 1 | 1.6 | 214 | 1600 | 12 |
| Flathead | MT | 5 | 2 | 1.5 | 192 | 200 | 13 |
| Gallatin | MT | 2 | 3 | 1.2 | 803 | 770 | 33 |
| Cascade | MT | 7 | 4 | 1.4 | 112 | 140 | 7 |
| Yellowstone | MT | 1 | 5 | 1.0 | 910 | 580 | 34 |
| Missoula | MT | 4 | 6 | 1.2 | 210 | 180 | 7 |
| Lake | MT | 6 | 7 | 1.1 | 150 | 500 | 8 |
| Lewis and Clark | MT | 8 | 8 | 1.1 | 110 | 160 | 5 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Teton | WY | 3 | 1 | 1.4 | 282 | 1220 | 12 |
| Lincoln | WY | 9 | 2 | 1.4 | 88 | 460 | 5 |
| Sweetwater | WY | 5 | 3 | 1.1 | 223 | 510 | 7 |
| Laramie | WY | 2 | 4 | 1.0 | 421 | 430 | 6 |
| Fremont | WY | 1 | 5 | 1.0 | 455 | 1140 | 5 |
| Albany | WY | 10 | 6 | 1.1 | 82 | 220 | 3 |
| Park | WY | 8 | 7 | 1.0 | 100 | 340 | 2 |
| Natrona | WY | 6 | 8 | 0.9 | 194 | 240 | 2 |
| Uinta | WY | 4 | 9 | 0.8 | 236 | 1150 | 2 |
| Campbell | WY | 7 | 10 | 0.8 | 108 | 230 | 2 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Canyon | ID | 2 | 1 | 1.2 | 4292 | 2020 | 187 |
| Ada | ID | 1 | 2 | 1.1 | 7358 | 1650 | 244 |
| Bonneville | ID | 6 | 3 | 1.5 | 516 | 460 | 32 |
| Kootenai | ID | 3 | 4 | 1.1 | 1366 | 890 | 52 |
| Jefferson | ID | 21 | 5 | 1.4 | 91 | 330 | 7 |
| Bannock | ID | 10 | 6 | 1.2 | 308 | 360 | 12 |
| Minidoka | ID | 9 | 7 | 1.2 | 372 | 1800 | 9 |
| Twin Falls | ID | 4 | 9 | 1.0 | 1041 | 1240 | 16 |
| Cassia | ID | 7 | 11 | 1.0 | 415 | 1760 | 7 |
| Jerome | ID | 8 | 19 | 0.7 | 381 | 1630 | 4 |
| Blaine | ID | 5 | 21 | 0.6 | 564 | 2560 | 1 |
Midwest
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Hancock | OH | 44 | 1 | 1.8 | 216 | 290 | 17 |
| Franklin | OH | 1 | 2 | 1.1 | 15903 | 1250 | 297 |
| Lucas | OH | 4 | 3 | 1.2 | 4081 | 940 | 94 |
| Cuyahoga | OH | 2 | 4 | 1.0 | 11844 | 940 | 197 |
| Montgomery | OH | 5 | 5 | 1.1 | 3528 | 660 | 82 |
| Licking | OH | 20 | 6 | 1.3 | 918 | 530 | 31 |
| Ross | OH | 43 | 7 | 1.5 | 258 | 330 | 12 |
| Hamilton | OH | 3 | 10 | 1.0 | 8598 | 1060 | 117 |
| Summit | OH | 6 | 13 | 1.1 | 2906 | 540 | 45 |
| Butler | OH | 8 | 15 | 1.1 | 2424 | 640 | 46 |
| Marion | OH | 7 | 43 | 1.1 | 2824 | 4320 | 6 |
| Pickaway | OH | 9 | 49 | 0.9 | 2314 | 4030 | 6 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Cook | IL | 1 | 1 | 1.1 | 102355 | 1960 | 536 |
| Saline | IL | 50 | 2 | 1.6 | 91 | 380 | 9 |
| DuPage | IL | 3 | 3 | 1.1 | 10861 | 1170 | 94 |
| St. Clair | IL | 6 | 4 | 1.1 | 3516 | 1330 | 82 |
| Madison | IL | 9 | 5 | 1.2 | 1815 | 680 | 53 |
| Lake | IL | 2 | 6 | 1.1 | 11411 | 1620 | 83 |
| Jackson | IL | 18 | 7 | 1.4 | 496 | 850 | 15 |
| Will | IL | 5 | 8 | 1.1 | 8094 | 1180 | 63 |
| Kane | IL | 4 | 13 | 1.1 | 8754 | 1650 | 46 |
| McHenry | IL | 8 | 16 | 1.1 | 2716 | 880 | 31 |
| Winnebago | IL | 7 | 17 | 1.1 | 3503 | 1220 | 25 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Marion | IN | 1 | 1 | 1.2 | 13754 | 1460 | 131 |
| Vanderburgh | IN | 10 | 2 | 1.2 | 1482 | 820 | 62 |
| Hamilton | IN | 6 | 3 | 1.2 | 2236 | 710 | 44 |
| Lake | IN | 2 | 4 | 1.1 | 6733 | 1380 | 82 |
| Dubois | IN | 28 | 5 | 1.3 | 551 | 1300 | 22 |
| St. Joseph | IN | 5 | 6 | 1.1 | 2820 | 1050 | 52 |
| Monroe | IN | 23 | 7 | 1.3 | 600 | 410 | 23 |
| Elkhart | IN | 3 | 8 | 1.0 | 4455 | 2190 | 57 |
| Allen | IN | 4 | 15 | 1.1 | 3365 | 910 | 31 |
| Johnson | IN | 9 | 20 | 1.1 | 1575 | 1040 | 16 |
| Hendricks | IN | 8 | 25 | 1.1 | 1663 | 1030 | 14 |
| Cass | IN | 7 | 26 | 1.2 | 1711 | 4490 | 6 |
Tennessee and Kentucky
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Henderson | TN | 39 | 1 | 1.7 | 324 | 1160 | 30 |
| Knox | TN | 5 | 2 | 1.4 | 3225 | 710 | 148 |
| Washington | TN | 23 | 3 | 1.5 | 674 | 530 | 45 |
| Shelby | TN | 2 | 4 | 1.1 | 18915 | 2020 | 390 |
| Roane | TN | 53 | 5 | 1.7 | 221 | 420 | 16 |
| Davidson | TN | 1 | 6 | 1.0 | 20081 | 2940 | 417 |
| Blount | TN | 18 | 7 | 1.5 | 815 | 630 | 50 |
| Rutherford | TN | 3 | 10 | 1.2 | 5418 | 1760 | 147 |
| Hamilton | TN | 4 | 12 | 1.1 | 5046 | 1410 | 128 |
| Sumner | TN | 7 | 21 | 1.1 | 2944 | 1640 | 77 |
| Williamson | TN | 6 | 23 | 1.0 | 3014 | 1380 | 89 |
| Wilson | TN | 8 | 28 | 1.1 | 1895 | 1430 | 52 |
| Trousdale | TN | 9 | 76 | 0.9 | 1565 | 16350 | 4 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Harlan | KY | 33 | 1 | 1.8 | 172 | 630 | 18 |
| Oldham | KY | 9 | 2 | 1.6 | 532 | 810 | 41 |
| Jefferson | KY | 1 | 3 | 1.3 | 6071 | 790 | 142 |
| Scott | KY | 24 | 4 | 1.6 | 246 | 460 | 14 |
| Boyle | KY | 50 | 5 | 1.6 | 97 | 320 | 6 |
| Barren | KY | 22 | 6 | 1.4 | 255 | 580 | 14 |
| Fayette | KY | 2 | 7 | 1.1 | 2812 | 880 | 64 |
| Warren | KY | 3 | 8 | 1.2 | 2215 | 1750 | 39 |
| Kenton | KY | 4 | 9 | 1.2 | 1195 | 730 | 27 |
| Boone | KY | 5 | 18 | 1.1 | 937 | 730 | 17 |
| Daviess | KY | 7 | 21 | 1.1 | 638 | 640 | 10 |
| Shelby | KY | 6 | 29 | 1.0 | 666 | 1420 | 8 |
| Muhlenberg | KY | 8 | 48 | 0.9 | 620 | 1990 | 4 |
Missouri and Arkansas
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Nodaway | MO | 49 | 1 | 2.3 | 80 | 350 | 9 |
| St. Louis | MO | 1 | 2 | 1.3 | 10821 | 1080 | 279 |
| Camden | MO | 33 | 3 | 1.8 | 184 | 410 | 13 |
| Polk | MO | 34 | 4 | 1.6 | 182 | 580 | 19 |
| St. Charles | MO | 3 | 5 | 1.2 | 3095 | 790 | 122 |
| St. Louis city | MO | 2 | 6 | 1.2 | 3971 | 1280 | 88 |
| Greene | MO | 7 | 7 | 1.3 | 1073 | 370 | 49 |
| Jackson | MO | 4 | 8 | 1.2 | 2690 | 390 | 81 |
| Jefferson | MO | 5 | 11 | 1.2 | 1179 | 530 | 40 |
| Jasper | MO | 6 | 17 | 1.1 | 1098 | 920 | 27 |
| Boone | MO | 8 | 21 | 1.0 | 1069 | 610 | 26 |
| Buchanan | MO | 9 | 37 | 1.0 | 1020 | 1150 | 6 |
| county | ST | case rank | severity | R_e | cases | cases/100k | daily cases |
|---|---|---|---|---|---|---|---|
| Newton | AR | 54 | 1 | 4.6 | 81 | 1030 | 21 |
| Independence | AR | 31 | 2 | 1.8 | 194 | 520 | 15 |
| Garland | AR | 17 | 3 | 1.4 | 662 | 670 | 33 |
| Pulaski | AR | 2 | 4 | 1.1 | 4405 | 1120 | 116 |
| Craighead | AR | 13 | 5 | 1.3 | 958 | 910 | 38 |
| Franklin | AR | 55 | 6 | 1.6 | 76 | 430 | 6 |
| Washington | AR | 1 | 7 | 1.1 | 5697 | 2490 | 85 |
| Sebastian | AR | 5 | 8 | 1.1 | 1425 | 1120 | 47 |
| Benton | AR | 3 | 10 | 1.0 | 4232 | 1630 | 59 |
| Crittenden | AR | 8 | 13 | 1.2 | 1085 | 2210 | 21 |
| Jefferson | AR | 6 | 15 | 1.1 | 1176 | 1670 | 26 |
| Faulkner | AR | 9 | 21 | 1.1 | 1069 | 870 | 23 |
| Lincoln | AR | 7 | 41 | 0.9 | 1157 | 8450 | 7 |
| Hot Spring | AR | 4 | 50 | 0.4 | 1463 | 4360 | 12 |
Conclusions
It’s in control some places, but not all places. And many places are completely out-of-control.
Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK
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2020-07-27 13:50:17
version history
Today is 2020-07-27.
68 days ago: Multiple states.
60 days ago: \(R_e\) computation.
57 days ago: created color coding for \(R_e\) plots.
52 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
52 days ago: “persistence” time evolution.
45 days ago: “In control” mapping.
45 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
37 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
32 days ago: Added Per Capita US Map.
30 days ago: Deprecated national map.
26 days ago: added state “Hot 10” analysis.
21 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
19 days ago: added per capita disease and mortaility to state-level analysis.
7 days ago: changed to county boundarieson national map for per capita disease.
2 days ago: corrected factor of two error in death trend data.
Appendix: Methods
Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov
Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.
\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis
Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]
\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.
Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…
Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation
\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]
Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.
DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.